The Study Of Artificial Bee Colony Algorithm And Application For Logistics Distribution Pouting Problem

The economy situation has been rising steadily and the development speed of e-commerce beyond our imagination with the reform and opening up of China. It directly stimulates the rapid development of China's logistics industry. A realistic problem: the city logistics distribution routing problem appeared. The initial distribution point we start from, the way to go through some of the key nodes and the path selection with minimum cost, which is the problem that this paper wants to solve. The artificial bee colony algorithm has the characteristics of simple parameter setting, high efficiency and robustness, so this paper uses artificial bee colony algorithm for solving urban logistics distribution routing problem. The main work of this paper is as follows:(1) Realistic problem and model transformation: First, we have abstracted the traffic network into undirected connected graph. Understanding the cross and vertical coordinates of each node and pointing out nodes which are the key points we must pass through in the graph. We label the weights of each edge according to the length and speed of each path. Using A* routing algorithm to find the shortest path between any two key nodes after labeling the weight of each edge. We abstract further and just save the location of key nodes which contain the shortest path information from that to any other key nodes, discard the location information of other node. At this point, the figure has been transformed as a fully connected graph. we find a path which goes through all the key nodes with minimum cost.(2) Initial path generation: we use insert optimization strategy. This strategy makes us obtain high quality solutions at the beginning stage of algorithm to find the optimal solution through the updating strategy of the path easier. This method is able to acquire faster convergence rate of the algorithm and highher quality of the feasible solution.(3)Algorithm verification: running simulation experiment on Matlab to verify the correctness of the algorithm. The algorithm in this pape acquire faster convergence rate of the algorithm and Stronger ability of question solving compared with the genetic algorithm.